An organization may use one or more computer systems. The different computer systems may be used for different purposes, by different people, and therefore each system may contain its own data.
Some such computer systems include business systems. Business systems can include, for instance, customer relations management (CRM) systems, enterprise resource planning (ERP) systems, line-of-business (LOB) systems, among others. These systems can store data records (such as entities) that represent items within the business system, and they can run business processes, workflows, or other business logic on the data records so that users can perform the tasks or activities in order to carry out the function of the business.
Entities can represent a wide variety of different types of things within a business system. They can be objects with callable functions, or they can be even more rich structures. In addition, they can represent a wide variety of different types of things. For instance, a customer entity can represent and describe a customer. A vendor entity can represent and describe a vendor. A product entity can represent and describe a product. A quote entity can represent and describe a quote. A business opportunity entity can represent and describe a business opportunity. These are examples only, and a wide variety of other entities can be used as well.
The data (e.g., entities or other business records) or other information can exist in disparate applications sourced for different business functions. Some of those functions can include, for instance, sales, marketing, customer service, e-commerce, among others. Because each of these different applications or systems has its own data, the data for a single entity may be different, depending upon the application in which it is used. For instance, the data representing customer A in a sales system may be different from the data representing customer A in a licensing system. In fact, it is not uncommon for these types of different representations to exist in many (perhaps 40-50 or more) different systems for a single enterprise or organization. This can present certain challenges.
For instance, it may be that a person from customer A contacts a customer service representative for an organization. The customer service representative may reside in some country where customer A does not have a large presence, and may not know that customer A is the organization's highest paying customer, because that information is stored in a sales system, while the customer service representative is using a customer service system. However, this type of information could be very useful to the customer service representative.
The problem can be exacerbated because many organizations have complicated relationships with one another. For instance, customer A may have a financial relationship with the organization, as well as a contractual or transactional relationship. The information needed in the financial relationship may be different from the information needed in the contractual relationship. Similarly, customer A may have certain usage patterns with the organization that are not captured in either the financial or contractual contexts. In some cases, customer A may be both a customer and a vendor of the same organization. All of these types of complicated relationships can make it even more difficult to understand, in a comprehensive sense, how customer A relates to the organization that deploys the business system.
Some work has been done in the area of entity resolution. This work has included attempts to perform object matching, duplicate identification, among other things. These entity resolution tasks are used in an attempt to identify different entities in the computer system that may be referring to the same real-world item. For instance, where a customer has a legal name of “ACME, Inc.” that term may be used to identify the customer in the licensing system. However, where the customer also has a different (e.g., popular) name, such as “The ACME Company”, that phrase may be used to identify the customer in the sales or customer service systems. Current work that is being done to perform entity resolution includes rule-based methods, pair-wise classification, various clustering approaches and different forms of probabilistic inference.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.